Mini Project #03: Visualizing and Maintaining the Green Canopy of NYC

Introduction: NYC Trees

Our project focuses on an understanding of the understanding of the attributes associated with trees. For example, tree density, tree vitality and tree population by species. Let’s take a look at NYC’s council districts to further our understanding the relationship between NYC’s council districts and their population of trees.

By the end of our project we will become New York arborists.

Data Aquisition

We download the data from the New York City Department of Planning to find our council district boundaries. The council district boundaries are given by the image below.

NYC Districts Data Set

  NYC_CDs <- function(dir_path = "data/mp03") {
  library(sf)
  library(tidyverse)
  library(httr2)
    

  # 1. Make sure the directory exists
  if (!dir.exists(dir_path)) {
    dir.create(dir_path, recursive = TRUE)
  }

  # 2. Download the zip file only if needed
  url <- "https://s-media.nyc.gov/agencies/dcp/assets/files/zip/data-tools/bytes/city-council/nycc_25c.zip"
  zip_path <- file.path(dir_path, "nycc_25c.zip")

  if (!file.exists(zip_path)) {
    download.file(url, destfile = zip_path, mode = "wb")
  }

  # 3. Unzip the contents into dir_path
  unzip(zip_path, exdir = dir_path)

  # 4. Read the .shp file with sf::st_read
  shp_file <- list.files(
    dir_path,
    pattern = "\\.shp$",
    full.names = TRUE,
    recursive = TRUE
  )

  if (length(shp_file) == 0) {
    stop("No .shp file found in ", dir_path)
  }

  nyc_raw <- sf::st_read(shp_file[1], quiet = TRUE)

  # 5. Transform to WGS 84
  nyc_wgs84 <- sf::st_transform(nyc_raw, crs = "WGS84")

  # 6. Return the transformed data
  return(nyc_wgs84)
}


cds <- NYC_CDs()



plot(sf::st_geometry(cds))

Next we download the data for NYC Forestry Tree Points. Note that this data does not include central park because Central Park is under separate management.

Tree Points Data Set

NYC_TreePoints <- function(dir_path    = "data/mp03",
                           limit       = 5000,
                           max_records = Inf) {
  # library(sf); library(dplyr); library(httr2) must be loaded before calling

  if (!dir.exists(dir_path)) {
    dir.create(dir_path, recursive = TRUE)
  }

  base_url <- "https://data.cityofnewyork.us/resource/hn5i-inap.geojson"

  offset       <- 0L
  chunk_index  <- 1L
  total_rows   <- 0L

  repeat {
    message("Chunk ", chunk_index,
            " | offset = ", offset,
            " | total_rows = ", total_rows)

    file_path <- file.path(
      dir_path,
      sprintf("treepoints_%05d.geojson", chunk_index)
    )

    # download this batch only if file doesn't already exist
    if (!file.exists(file_path)) {
      req <- request(base_url) |>
        req_url_query(
          `$limit`  = limit,
          `$offset` = offset
        )

      resp <- req_perform(req)
      geojson_text <- resp_body_string(resp)
      writeLines(geojson_text, file_path)
    }

    sf_chunk <- sf::st_read(file_path, quiet = TRUE)

    if (nrow(sf_chunk) == 0) {
      message("No rows returned; stopping.")
      break
    }

    total_rows <- total_rows + nrow(sf_chunk)

    if (nrow(sf_chunk) < limit) {
      message("Last (partial) batch from API; stopping.")
      break
    }

    if (total_rows >= max_records) {
      message("Reached max_records = ", max_records, "; stopping early.")
      break
    }

    offset      <- offset + limit
    chunk_index <- chunk_index + 1L
  }

  # 8) Read all GeoJSON data files using st_read
  geojson_files <- list.files(
    dir_path,
    pattern = "^treepoints_.*\\.geojson$",
    full.names = TRUE
  )

  if (length(geojson_files) == 0) {
    stop("No GeoJSON files found in ", dir_path)
  }

  chunks <- lapply(geojson_files, function(f) {
    sf::st_read(f, quiet = TRUE)
  })

  # Separate attributes and geometry
  data_list <- lapply(chunks, sf::st_drop_geometry)
  geom_list <- lapply(chunks, sf::st_geometry)
  crs_obj   <- sf::st_crs(chunks[[1]])

  # Harmonize columns and types for bind_rows
  all_cols <- unique(unlist(lapply(data_list, names)))
  data_list <- lapply(data_list, function(df) {
    missing <- setdiff(all_cols, names(df))
    for (m in missing) df[[m]] <- NA_character_
    df <- df[, all_cols, drop = FALSE]
    df[] <- lapply(df, as.character)
    df
  })

  # Combine all data sets using bind_rows
  trees_all_df <- dplyr::bind_rows(data_list)

  # Combine geometry and rebuild sf
  geom_all <- do.call(c, geom_list)
  trees_all <- sf::st_as_sf(
    trees_all_df,
    geometry = geom_all,
    crs      = crs_obj
  )

  return(trees_all)
}

# <- function ends here. NOW you can call it:

trees_full<- NYC_TreePoints(limit = 5000, max_records = Inf)

Map of Trees in All Districts

library(sf)
library(dplyr)
library(ggplot2)

# Make sure trees have lon/lat extracted
trees_pts <- trees_full |>
  mutate(
    lon = st_coordinates(geometry)[, 1],
    lat = st_coordinates(geometry)[, 2]
  )

# Bounding box for the districts
bb <- st_bbox(cds)

ggplot() +
  
  # 1) Council district outlines
  geom_sf(
    data       = cds,
    fill       = NA,
    color      = "grey20",
    linewidth  = 0.4
  ) +
  
  # 2) All tree points in green
  geom_point(
    data  = trees_pts,
    aes(x = lon, y = lat),
    color = "#2ca25f",     # green
    alpha = 0.35,
    size  = 0.2
  ) +
  
  coord_sf(
    crs  = 4326,
    xlim = c(bb["xmin"], bb["xmax"]),
    ylim = c(bb["ymin"], bb["ymax"]),
    expand = FALSE
  ) +
  
  labs(
    title    = "NYC Tree Locations",
    subtitle = "All recorded tree points plotted in green",
    x        = "Longitude",
    y        = "Latitude",
    caption  = "Data: NYC OpenData Forestry Tree Points (hn5i-inap) & NYC Council Districts"
  ) +
  
  theme_minimal() +
  theme(
    plot.background   = element_rect(fill = "white", color = NA),
    panel.background  = element_rect(fill = "white", color = NA),
    panel.grid        = element_line(color = "grey90"),
    axis.text         = element_text(color = "grey20"),
    axis.title        = element_text(color = "grey10"),
    legend.position   = "none",
    plot.title        = element_text(size = 18, face = "bold"),
    plot.subtitle     = element_text(size = 12)
  )

In this map we include all of the tree point within all of our community districts, NYC has a lot of trees. It’s not possible to see each indicidual tree point in our plot.

District Level Analysis of Trees

Now we move to our exploratory analysis. We will explore New York City’s trees on a more localized scale.

Which council district has the most trees?

library(sf)
library(dplyr)
library(DT)

# 1. Align CRS for cds and trees_full
if (!is.na(st_crs(cds)) && !is.na(st_crs(trees_full))) {
  if (st_crs(trees_full) != st_crs(cds)) {
    trees_full <- st_transform(trees_full, st_crs(cds))
  }
} else {
  cds        <- st_transform(cds, 4326)
  trees_full <- st_transform(trees_full, 4326)
}

# 2. Use the REAL council district column from cds
#    Replace "CounDist" with the actual name if it's different.
cds_for_join <- cds |>
  rename(council_dist = CounDist) |>
  select(council_dist, geometry)

# 3. Spatial join: every tree gets a council_dist
trees_with_district <- st_join(
  trees_full,
  cds_for_join,
  join = st_intersects,
  left = TRUE
)

# 1) Count trees per council district
trees_by_district <- trees_with_district |>
  st_drop_geometry() |>
  count(council_dist, name = "tree_count")

# 2) Take top 10 by tree count
top10_cd <- trees_by_district |>
  arrange(desc(tree_count)) |>
  slice_head(n = 10)

# 3) Build datatable: district number + tree count
result <- top10_cd |>
  transmute(
    `Council District Number` = council_dist,
    `Tree Count`              = tree_count
  ) |>
  datatable(
    rownames = FALSE,
    class    = "compact",
    options  = list(
      dom      = "t",
      paging   = FALSE,
      ordering = FALSE,
      info     = FALSE
    )
  )

result

Council District 51 has the most Trees with 70,965 trees.

Which council district has the highest density of trees?

library(dplyr)
library(sf)
library(DT)

# 1. Count trees per council district
trees_by_district <- trees_with_district |>
  st_drop_geometry() |>
  filter(!is.na(council_dist)) |>
  count(council_dist, name = "tree_count")

# 2. Join counts back onto districts
cds_density <- cds |>
  rename(
    council_dist = CounDist,
    total_area    = Shape_Area
  ) |>
  left_join(trees_by_district, by = "council_dist") |>
  mutate(
    tree_density = tree_count / total_area   # NO conversions
  )

# 3. Sort and output the densest district
cds_density_sorted <- cds_density |>
  arrange(desc(tree_density)) |>
  st_drop_geometry()

result <- datatable(
  cds_density_sorted |>
    select(council_dist, tree_count, total_area, tree_density) |>
    slice_head(n = 1),
  rownames = FALSE,
  class    = "compact",
  options  = list(
    dom = 't',
    paging = FALSE,
    ordering = FALSE,
    info = FALSE,
    columnDefs = list(
      list(className = "dt-center", targets = "_all")
    )
  )
)

result

Council District 7 has the highest density of trees.

Plot of Density in District 7

library(sf)
library(dplyr)
library(ggplot2)
library(MASS)    # kde2d
library(scales)  # rescale

# 1) Trees in Council District 7
trees_cd7 <- trees_with_district |>
  filter(council_dist == 7)

coords_cd7 <- st_coordinates(trees_cd7)
lon <- coords_cd7[, 1]
lat <- coords_cd7[, 2]

# 2) KDE on lon/lat
dens <- MASS::kde2d(
  x = lon,
  y = lat,
  n = 150   # smaller = faster; increase later if you want smoother
)

# Turn KDE grid into a data.frame of points
grid_df <- expand.grid(
  lon = dens$x,
  lat = dens$y
)
grid_df$density <- as.vector(dens$z)

# Convert to sf points
density_pts <- st_as_sf(
  grid_df,
  coords = c("lon", "lat"),
  crs = st_crs(trees_cd7)
)

# 3) Keep only density points inside District 7
cd7_poly <- cds |> filter(CounDist == 7)

inside <- st_within(density_pts, cd7_poly, sparse = FALSE)[, 1]
density_cd7 <- density_pts[inside, ]

# Pull back lon/lat for ggplot
density_cd7_df <- density_cd7 |>
  mutate(
    lon = st_coordinates(geometry)[, 1],
    lat = st_coordinates(geometry)[, 2]
  ) |>
  st_drop_geometry()

# Normalize density 0–1
density_cd7_df$density_norm <- scales::rescale(density_cd7_df$density)

# 4) Zoom box around District 7
bbox <- st_bbox(cd7_poly)
x_range <- bbox$xmax - bbox$xmin
y_range <- bbox$ymax - bbox$ymin   # <-- this was the bug

x_lim <- c(bbox$xmin - 0.1 * x_range,
           bbox$xmax + 0.1 * x_range)
y_lim <- c(bbox$ymin - 0.1 * y_range,
           bbox$ymax + 0.1 * y_range)

# 5) Plot
ggplot() +
  # NYC council districts background
  geom_sf(data = cds, fill = "grey95", color = "grey80") +

  # density tiles only where points are inside CD 7
  geom_raster(
    data = density_cd7_df,
    aes(x = lon, y = lat, fill = density_norm),
    interpolate = TRUE
  ) +

  # outline for District 7
  geom_sf(
    data = cd7_poly,
    fill = NA,
    color = "black",
    linewidth = 1
  ) +

  scale_fill_gradientn(
    colours = c(
      "#2d004b",
      "#54278f",
      "#756bb1",
      "#f768a1",
      "#fe9929",
      "#ffff99"
    ),
    name = "Tree density\n(relative)"
  ) +

  coord_sf(xlim = x_lim, ylim = y_lim, expand = FALSE) +
  labs(
    title    = "Tree Density in NYC Council District 7",
    subtitle = "Zoomed-in heatmap, masked to district boundary",
    x        = "Longitude",
    y        = "Latitude"
  ) +
  theme_minimal() +
  theme(
    panel.background = element_rect(fill = "white", color = NA),
    plot.background  = element_rect(fill = "white", color = NA),
    legend.position  = "right"
  )

This map shows the relative density of trees in district 7. We can see that there are clear hotspots throughout district 7. We can assume these hotspots contribute to the overall density of district 7.

Which district has highest fraction of dead trees out of all trees?

library(dplyr)
library(DT)
library(sf)

# 1) Drop geometry to work with plain columns
trees_df <- trees_with_district |> st_drop_geometry()

# 2) Normalize condition text (just in case)
trees_df <- trees_df |>
  mutate(tpcondition = toupper(tpcondition))

# 3) Count alive vs dead per council district
dead_stats <- trees_df |>
  group_by(council_dist) |>
  summarise(
    total_trees = n(),
    dead_trees  = sum(tpcondition %in% c("DEAD", "STUMP", "REMOVED"), na.rm = TRUE),
    dead_fraction = dead_trees / total_trees
  ) |>
  arrange(desc(dead_fraction))

# 4) Display as datatable (top district only)
result <- dead_stats |>
  slice_head(n = 1) |>
  datatable(
    rownames = FALSE,
    class = "compact",
    options = list(
      dom = "t",
      paging = FALSE,
      ordering = FALSE,
      info = FALSE
    )
  )

result

District 32 has the highest fraction of dead trees at approximately 14.255%.

Map of Dead Trees in District 32

library(dplyr)
library(sf)
library(ggplot2)
library(scales)

# 1. Dead trees in District 32
dead32 <- trees_with_district |>
  filter(
    council_dist == 32,
    toupper(tpcondition) %in% c("DEAD", "STUMP", "REMOVED")
  )

n_dead <- nrow(dead32)

# 2. District 32 polygon
cd32_poly <- cds |> filter(CounDist == 32)

# 3. Zoom window with padding
bbox32   <- st_bbox(cd32_poly)
x_pad    <- (bbox32$xmax - bbox32$xmin) * 0.08
y_pad    <- (bbox32$ymax - bbox32$ymin) * 0.08

x_min <- bbox32$xmin - x_pad
x_max <- bbox32$xmax + x_pad
y_min <- bbox32$ymin - y_pad
y_max <- bbox32$ymax + y_pad

# 4. Cleaner plot
ggplot() +
  # all districts for context, very light
  geom_sf(data = cds, fill = "grey95", color = "grey85", linewidth = 0.3) +
  
  # District 32 highlighted
  geom_sf(data = cd32_poly, fill = "grey98", color = "black", linewidth = 1) +
  
  # dead trees in 32
  geom_sf(
    data = dead32,
    color = "#e63946",
    alpha = 0.6,
    size  = 0.7
  ) +
  
  coord_sf(
    xlim = c(x_min, x_max),
    ylim = c(y_min, y_max),
    expand = FALSE
  ) +
  
  labs(
    title    = "Dead Trees in NYC Council District 32",
    subtitle = paste("Zoomed map of", comma(n_dead), "dead / stump / removed trees"),
    x        = "Longitude",
    y        = "Latitude"
  ) +
  
  theme_minimal(base_size = 12) +
  theme(
    panel.background = element_rect(fill = "white", color = NA),
    plot.background  = element_rect(fill = "white", color = NA),
    panel.grid.major = element_line(color = "grey90", linewidth = 0.2),
    panel.grid.minor = element_blank(),
    plot.title       = element_text(face = "bold", size = 16),
    plot.subtitle    = element_text(size = 11),
    axis.title       = element_text(size = 11),
    axis.text        = element_text(size = 9),
    axis.text.x      = element_text(angle = 45, hjust = 1)
  )

Our map of District 32 plots the dead trees throughout the district. Since this district is closer to the ocean, it makes sense that the soil may not be suitable to host the normal species in NYC.

What is the most common tree species in Manhattan?

library(dplyr)
library(sf)
library(DT)

# Manhattan trees (CD 1–10), drop geometry
manhattan_trees <- trees_with_district |>
  filter(council_dist %in% 1:10) |>
  st_drop_geometry()

# Extract common name and count
species_counts_common <- manhattan_trees |>
  mutate(
    common_name = sub("^.* -\\s*", "", genusspecies)
  ) |>
  count(common_name, sort = TRUE, name = "tree_count")

# Show top 5 as datatable
result <- species_counts_common |>
  slice_head(n = 5) |>
  datatable(
    rownames = FALSE,
    class = "compact",
    options = list(
      dom = "t",
      paging = FALSE,
      ordering = TRUE,
      info = FALSE
    )
  )

result

The Thornlesss Honeylocust is the most common tree in Manhattan, with 17,310 trees within the city.

Top Five Trees in Manhattan Map

library(sf)
library(dplyr)
library(leaflet)

# 0) Make sure we’re in WGS84 for leaflet
trees_wgs <- st_transform(trees_with_district, 4326)
cds_wgs   <- st_transform(cds, 4326)

# 1) Define Manhattan as council districts 1–10
manhattan_sf <- trees_wgs |>
  filter(council_dist %in% 1:10) |>
  mutate(
    # Pull out common name after the " - "
    common_name = sub("^.*-\\s*", "", genusspecies)
  )

# 2) Find top 5 most common species in Manhattan
top5_species <- manhattan_sf |>
  st_drop_geometry() |>
  count(common_name, sort = TRUE) |>
  slice_head(n = 5) |>
  pull(common_name)

# 3) Keep only those trees
mt5_wgs <- manhattan_sf |>
  filter(common_name %in% top5_species)

# 4) Color palette for the 5 species
pal <- colorFactor(
  palette = "Set2",
  domain  = sort(unique(mt5_wgs$common_name))
)

# 5) Leaflet map – back to simple fixed-size circle markers
leaflet() |>
  addProviderTiles(providers$CartoDB.Positron) |>

  # NYC council districts for context
  addPolygons(
    data   = cds_wgs,
    weight = 1,
    color  = "grey40",
    fill   = FALSE
  ) |>

  # Top-5 Manhattan trees as colored points
  addCircleMarkers(
    data        = mt5_wgs,
    radius      = 3,                  # fixed pixel radius
    stroke      = FALSE,
    fillOpacity = 0.7,
    color       = ~pal(common_name),
    popup       = ~paste0(
      "Species: ", common_name, "<br>",
      "Council district: ", council_dist
    )
  ) |>

  addLegend(
    position = "bottomright",
    pal      = pal,
    values   = mt5_wgs$common_name,
    title    = "Top 5 species"
  ) |>

  # Center roughly on Manhattan
  setView(lng = -73.97, lat = 40.78, zoom = 12)

I’ve plotted the top 5 trees in Manhattan for reference. Note that the Honeylocust is plotted in green.

Closest Tree to Baruch College

library(dplyr)
library(sf)
library(DT)

# 1. Helper to make a WGS84 point
new_st_point <- function(lat, lon) {
  sf::st_sfc(sf::st_point(c(lon, lat)), crs = "WGS84")
}

# 2. Baruch College point
baruch_point <- new_st_point(
  lat = 40.7403,
  lon = -73.9831
)

# 3. Distance (in meters) from every tree to Baruch
trees_with_distance <- trees_with_district |>
  dplyr::mutate(
    distance_m = as.numeric(sf::st_distance(geometry, baruch_point))
  )

# 4. Closest tree: one row, common name + distance
closest_tree <- trees_with_distance |>
  dplyr::slice_min(distance_m, n = 1) |>
  sf::st_drop_geometry() |>
  dplyr::mutate(
    common_name = sub(".* - ", "", genusspecies)  # keep only part after " - "
  ) |>
  dplyr::select(common_name, distance_m)

# 5. Single-row datatable
DT::datatable(
  closest_tree,
  rownames = FALSE,
  class    = "compact",
  options  = list(
    dom      = "t",
    paging   = FALSE,
    ordering = FALSE,
    info     = FALSE
  )
)

The closest tree to Baruch College is the Callery Pear, at 15.49 meters away.

Callery Pear Trees around Baruch College

library(dplyr)
library(sf)
library(leaflet)

# Callery pear color to match previous map
CALLERY_PEAR_COLOR <- "#66C2A5" 

# Baruch College coordinates
baruch_point <- sf::st_sfc(sf::st_point(c(-73.9831, 40.7403)), crs = 4326) # WGS84

# Defining radius 
radius_m <- 500 



# Map Style icon
baruch_icon <- makeAwesomeIcon(
  icon = "fa-university",     
  library = "fa",
  markerColor = "red",       
  iconColor = "white"        
)


callery_pear_nearby <- trees_with_district |>
  # Calculate distance (in meters) from every tree to Baruch
  dplyr::mutate(
    distance_m = as.numeric(sf::st_distance(geometry, baruch_point))
  ) |>
  # Filter for Callery Pears
  dplyr::filter(grepl("Callery pear", genusspecies, ignore.case = TRUE)) |>
  # 📍 Filter for trees within the specified radius
  dplyr::filter(distance_m <= radius_m)




callery_pear_nearby_wgs84 <- sf::st_transform(callery_pear_nearby, crs = 4326)

# Create a leaflet map
leaflet() |>
  addProviderTiles(providers$CartoDB.Positron) |>
  
  # Add markers for the Callery Pear trees (still using Circle Markers for consistency)
  addCircleMarkers(
    data = callery_pear_nearby_wgs84,
    radius = 4,
    color = CALLERY_PEAR_COLOR,
    stroke = FALSE,
    fillOpacity = 0.8,
    popup = ~paste(
      "Species: ", genusspecies, "<br>",
      "Distance: ", round(distance_m, 1), " m"
    )
  ) |>
  
  # Add the Baruch College marker using addAwesomeMarkers() for the custom icon and hover label
  addAwesomeMarkers(
    lng = sf::st_coordinates(baruch_point)[, 1],
    lat = sf::st_coordinates(baruch_point)[, 2],
    icon = baruch_icon,               # Use the custom pin icon
    label = "Baruch College",         # The text that appears on hover 
    popup = "Baruch College (NYC)",   # The text that appears on click
    layerId = "Baruch College"
  ) |>
  
  # Add a circle to visualize the search radius
  addCircles(
    lng = sf::st_coordinates(baruch_point)[, 1],
    lat = sf::st_coordinates(baruch_point)[, 2],
    weight = 1,
    radius = radius_m,
    color = "blue",
    fillOpacity = 0.1
  ) |>
  
  # Set view to the area around Baruch College
  setView(lng = -73.9831, lat = 40.7403, zoom = 15)

This interactive map shows all the Callery Pear trees within a 500 meter radius of Baruch. If we zoom in, we can actually find the closest tree to Baruch’s coordinates.

Callery Pear Tree

Downloading NYC Parks & Recreation Data Sets

Again we are downloading 2 massive datasets, but we take the same approach as we did for the Tree Points data set. We recieve the data in chuncks.

library(httr2)
library(readr)
library(dplyr)

download_socrata_csv <- function(resource_id,
                                 prefix,
                                 dir_path    = "data/mp03",
                                 limit       = 50000,
                                 max_records = Inf) {
  # Ensure directory exists
  if (!dir.exists(dir_path)) {
    dir.create(dir_path, recursive = TRUE)
  }

  base_url <- paste0("https://data.cityofnewyork.us/resource/", resource_id, ".csv")

  offset      <- 0L
  batch_index <- 1L
  total_rows  <- 0L
  chunks      <- list()

  repeat {
    message("Batch ", batch_index,
            " | offset = ", offset,
            " | total_rows = ", total_rows)

    file_path <- file.path(
      dir_path,
      sprintf("%s_%05d.csv", prefix, batch_index)
    )

    # Only hit the API if the file isn't already saved
    if (!file.exists(file_path)) {
      req <- request(base_url) |>
        req_url_query(
          `$limit`  = limit,
          `$offset` = offset
        )

      resp <- req_perform(req)
      csv_text <- resp_body_string(resp)
      writeLines(csv_text, file_path)
    }

    
    batch_df <- readr::read_csv(
      file_path,
      col_types      = cols(.default = col_character()),
      show_col_types = FALSE,
      progress       = FALSE
    )

    if (nrow(batch_df) == 0) {
      message("No rows returned; stopping.")
      break
    }

    chunks[[batch_index]] <- batch_df
    total_rows <- total_rows + nrow(batch_df)

    if (nrow(batch_df) < limit) {
      message("Last (partial) batch from API; stopping.")
      break
    }

    if (total_rows >= max_records) {
      message("Reached max_records = ", max_records, "; stopping early.")
      break
    }

    offset      <- offset + limit
    batch_index <- batch_index + 1L
  }

  if (length(chunks) == 0) {
    stop("No data downloaded or loaded for resource_id = ", resource_id)
  }

  dplyr::bind_rows(chunks)
}



parks_risk <- download_socrata_csv(
  resource_id = "259a-b6s7",
  prefix      = "parks_risk",
  dir_path    = "data/mp03",
  limit       = 50000,
  max_records = Inf
)

parks_work <- download_socrata_csv(
  resource_id = "bdjm-n7q4",
  prefix      = "parks_work",
  dir_path    = "data/mp03",
  limit       = 50000,
  max_records = Inf
)

With these new data sets from NYC’s Parks and Recreation we can find out information about work orders, and the overall safety of trees across our council districts.

Task Policy

District 6 Tree Safety & Fall Festival Initiative

District 6 is home to some of Manhattan’s most neighborhoods and features, representing Hell’s Kitchen, The Upper west Side and Central Park it is the perfect district to host a fall festival. Our Fall Festival Initiative aims to promote appreciation of NYC’s trees and bring awareness to the overall need for tree maintenance and safety of our trees. While our festival will be celebrating fall colors, we will also be honoring the work done by NYC’s Parks and Recreation Department.

The Top 5 trees in, shown in the image above, are The Honeylocust, Pin Oak, London Planetree, Maidenhair, and American elm. These trees produce iconic fall colors and we will be highlighting these trees during our festival, but all of trees in District 6 will be appreciated.

The following chart and map highlight the prevalence of these trees in District 6.

Chart of Fall Color Trees in District 6

library(dplyr)
library(sf)
library(ggplot2)
library(stringr)

# 1. Filter to District 6 and get top 5 species
top5_cd6 <- trees_with_district |>
  st_drop_geometry() |>
  filter(as.integer(council_dist) == 6) |>
  count(genusspecies, name = "count", sort = TRUE) |>
  slice_head(n = 5) |>
  mutate(
    # extract common name after the dash
    common_name = str_trim(str_extract(genusspecies, "(?<=- ).*"))
  )

# 2. Vertical color-coded bar chart with legend of common names
ggplot(top5_cd6, aes(x = reorder(common_name, count), y = count, fill = common_name)) +
  geom_col() +
  labs(
    title = "Top 5 Tree Species in Manhattan District 6",
    x = "Common Name",
    y = "Number of Trees",
    fill = "Species (Common Name)"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    legend.position = "right",
    axis.text.x = element_text(angle = 20, hjust = 1)
  )

Map of Fall Color Trees in District 6

library(sf)
library(dplyr)
library(leaflet)

# WGS84 for leaflet 
trees_wgs <- st_transform(trees_with_district, 4326)
cds_wgs   <- st_transform(cds, 4326)

# District 6, with common name 
cd6_trees <- trees_wgs |>
  filter(as.integer(council_dist) == 6) |>
  mutate(
    
    common_name = sub("^.*-\\s*", "", genusspecies)
  )

# Top 5 most common species in District 6 
top5_species_cd6 <- cd6_trees |>
  st_drop_geometry() |>
  count(common_name, sort = TRUE, name = "count") |>
  slice_head(n = 5) |>
  pull(common_name)

# Keep only trees from those 5 species 
cd6_top5 <- cd6_trees |>
  filter(common_name %in% top5_species_cd6)

# Color palette for the 5 species 
pal <- colorFactor(
  palette = "Set2",
  domain  = unname(sort(unique(cd6_top5$common_name)))
)

# Interactive map
leaflet() |>
  addProviderTiles(providers$CartoDB.Positron) |>

  # NYC council districts for context
  addPolygons(
    data   = cds_wgs,
    weight = 1,
    color  = "grey40",
    fill   = FALSE
  ) |>

  # Top-5 District 6 trees as colored points
  addCircleMarkers(
    data        = cd6_top5,
    radius      = 3,
    stroke      = FALSE,
    fillOpacity = 0.7,
    color       = ~pal(common_name),
    popup       = ~paste0(
      "Species: ", common_name, "<br>",
      "Council district: ", council_dist
    )
  ) |>

 
  addLegend(
    position = "bottomright",
    pal      = pal,
    data     = cd6_top5,
    values   = ~common_name,
    title    = "Top 5 species (CD 6)"
  ) |>

  # Center around District 6
  setView(lng = -73.98, lat = 40.78, zoom = 13)

In preparation for our Fall Festival, we analyzed the overall risk of these trees. This bring us to the next part of our proposal: why District 6? District 6 has the highest number of high risk trees in Manhattan at 2,967. Out of all the records from the Parks and Recreation department, 24% involved high risk trees. With 5,481 open work orders, District 6 is in desperate need of maintenance.

Work Orders and High Risk Trees from NYC Parks Data Sets

library(dplyr)
library(stringr)
library(sf)
library(DT)

# Work Orders Manhattan only

wo_manh <- parks_work |>
  st_drop_geometry() |>
  as.data.frame() |>
  filter(boroughcode == "Manhattan",
         !is.na(citycouncil))

# make district a clean integer
wo_manh <- wo_manh |>
  mutate(council_dist = as.integer(round(as.numeric(citycouncil))))

maintenance_keywords <- "(?i)prune|remove|hazard|branch|stump|clean|risk"

work_summary <- wo_manh |>
  group_by(council_dist) |>
  summarize(
    total_work_orders    = n(),
    open_work_orders     = sum(str_detect(wostatus, "(?i)open"), na.rm = TRUE),
    maint_related_orders = sum(str_detect(wocategory, maintenance_keywords), na.rm = TRUE),
    .groups = "drop"
  )

# Risk analysis


inspect_to_dist <- wo_manh |>
  distinct(inspectionglobalid, council_dist) |>
  filter(!is.na(inspectionglobalid))

risk_join <- parks_risk |>
  st_drop_geometry() |>
  as.data.frame() |>
  left_join(inspect_to_dist, by = "inspectionglobalid") |>
  filter(!is.na(council_dist))

# riskrating is coded as numbers in strings; convert to numeric
risk_join <- risk_join |>
  mutate(riskrating_num = suppressWarnings(as.numeric(riskrating)))

# choose a threshold for “elevated risk”; here ≥8 is treated as higher risk
risk_summary <- risk_join |>
  group_by(council_dist) |>
  summarize(
    total_risk_records = n(),
    elevated_risk      = sum(riskrating_num >= 8, na.rm = TRUE),
    .groups = "drop"
  ) |>
  mutate(
    pct_elevated = round(100 * elevated_risk / total_risk_records, 1)
  )

# Combine work order and risk

full_summary <- work_summary |>
  full_join(risk_summary, by = "council_dist") |>
  arrange(council_dist)

# replace NAs with 0 
full_summary <- full_summary |>
  mutate(
    total_work_orders    = replace_na(total_work_orders, 0L),
    open_work_orders     = replace_na(open_work_orders, 0L),
    maint_related_orders = replace_na(maint_related_orders, 0L),
    total_risk_records   = replace_na(total_risk_records, 0L),
    elevated_risk        = replace_na(elevated_risk, 0L),
    pct_elevated         = replace_na(pct_elevated, 0)
  )

## Combined table

result <- datatable(
  full_summary,
  colnames = c(
    "Council District",
    "Total Work Orders",
    "Open Work Orders",
    "Maintenance-Related Orders",
    "Total Risk Records (Inspections)",
    "High-Risk Score Trees (rating ≥ 8)",
    "% High-Risk Score Trees"
  ),
  caption = "Manhattan Tree Work Orders and Risk Assessments by Council District",
  options = list(dom = "t", paging = FALSE)
)

result

The following chart shows that district 6 greatly exceeds district 7 and district 8, the two districts with the highest number of total trees, in terms of high risk trees. The following chart shows the tree counts for each district in Manhattan. We can assume that district 6 does not have the highest percentage of risky trees just because it has a high total number of trees. District 6 falls at number 6 on our chart, so, the amount of risky trees in district 6 come from attributes unique to district 6.

library(dplyr)
library(DT)

# Count total trees in each Manhattan district
top3_total_trees <- trees_with_district |>
  st_drop_geometry() |>
  filter(as.integer(council_dist) %in% 1:10) |>   # Manhattan only
  count(council_dist, name = "total_trees", sort = TRUE) |>
  slice_head(n = 10)

# Format as datatable
result_top3_trees <- datatable(
  top3_total_trees,
  rownames = FALSE,
  class = "compact",
  options = list(dom = 't', paging = FALSE, ordering = TRUE, info = FALSE)
)

result_top3_trees

Comparison of Risky Trees in District 6, 7 and 8

library(dplyr)
library(ggplot2)

# Filter to districts 6, 7, and 8
risk_compare <- full_summary |>
  filter(council_dist %in% c(6, 7, 8))


# Bar chart: high-risk tree counts 
ggplot(risk_compare,
       aes(x = factor(council_dist),
           y = elevated_risk)) +
  geom_col(fill = "red") +
  labs(
    title = "High-Risk Trees (Risk ≥ 8): Districts 6, 7, and 8",
    x = "Council District",
    y = "Number of High-Risk Trees"
  ) +
  theme_minimal(base_size = 14)

Interactive Map of High Risk Trees in District 6

# Convert geometry to lon/lat 
library(sf)
library(dplyr)
library(leaflet)

# District 6 trees with riskrating 
trees_d6_high <- trees_with_district |>
  mutate(riskrating_num = suppressWarnings(as.numeric(riskrating))) |>
  filter(
    as.integer(council_dist) == 6,        # District 6 only
    !is.na(riskrating_num),
    riskrating_num >= 8                   # high-risk
  ) |>
  st_transform(4326)


# District 6 polygon 
cd6_poly <- cds |>
  st_transform(4326) |>
  filter(as.integer(CounDist) == 6)

# Interactive map District 6, high-risk trees in red 
leaflet() |>
  addProviderTiles(providers$CartoDB.Positron) |>

  # District 6 boundary
  addPolygons(
    data   = cd6_poly,
    color  = "black",
    weight = 2,
    fill   = FALSE
  ) |>

  # High-risk trees
  addCircleMarkers(
    data        = trees_d6_high,
    radius      = 4,
    color       = "red",
    fillOpacity = 0.8,
    stroke      = FALSE,
    popup       = ~paste0(
      "<b>Risk rating:</b> ", riskrating_num, "<br>",
      "<b>Condition:</b> ", tpcondition, "<br>",
      "<b>Species:</b> ", genusspecies
    )
  ) |>

  # View on District 6 (upper west / central park area)
  setView(lng = -73.98, lat = 40.78, zoom = 13)

Our map highlights the locations of these high risk trees.

Mean Risk of Manhattan Trees

library(dplyr)
library(sf)
library(DT)
library(stringr)
library(tidyr)

# Trees_with_district Manhattan (districts 1–10)
trees_manh <- trees_with_district |>
  st_drop_geometry() |>
  mutate(
    council_dist   = as.integer(council_dist),
    tpcondition    = str_to_title(tpcondition),
    riskrating_num = as.numeric(riskrating)
  ) |>
  filter(council_dist %in% 1:10)

# Condition by district
condition_summary <- trees_manh |>
  group_by(council_dist, tpcondition) |>
  tally(name = "count") |>
  pivot_wider(
    names_from  = tpcondition,
    values_from = count,
    values_fill = 0
  )

# risk by district
risk_summary <- trees_manh |>
  group_by(council_dist) |>
  summarize(
    total_trees    = n(),
    mean_risk      = round(mean(riskrating_num, na.rm = TRUE), 2),
    elevated_risk  = sum(riskrating_num >= 8,  na.rm = TRUE),  # 8–12
    very_high_risk = sum(riskrating_num >= 10, na.rm = TRUE),  # 10–12
    pct_elevated   = round(100 * elevated_risk / total_trees, 1),
    .groups = "drop"
  )

# Combine condition and risk 
combined_summary <- risk_summary |>
  left_join(condition_summary, by = "council_dist") |>
  arrange(council_dist)

# Datatable
result <- datatable(
  combined_summary,
  caption = "Tree Condition and Risk Rating by Manhattan Council District",
  options = list(dom = "t", paging = FALSE)
)

result

Lolipop Chart for Mean Risk of Manhattan Trees

library(dplyr)
library(ggplot2)

# Lollipop chart of mean risk, highlighting District 6
risk_summary |>
  mutate(
    dist_factor = factor(council_dist),
    is_6        = council_dist == 6
  ) |>
  ggplot(aes(x = mean_risk, y = dist_factor, color = is_6)) +
  # stick ("lollipop" line)
  geom_segment(aes(x = 0, xend = mean_risk, 
                   y = dist_factor, yend = dist_factor),
               linewidth = 1) +
  # candy ("lollipop" point)
  geom_point(size = 3) +
  scale_color_manual(
    values = c(`TRUE` = "red", `FALSE` = "grey60"),
    labels = c(`TRUE` = "District 6", `FALSE` = "Other districts"),
    name   = ""
  ) +
  labs(
    title = "Average Tree Risk Score by Manhattan Council District",
    x     = "Mean Risk Rating",
    y     = "Council District"
  ) +
  theme_minimal(base_size = 14)

District 6 also exceeds the surrounding Manhattan districts in terms of mean risk as shown by this chart. With our upcoming fall festival, we suggest pruning 1000+ trees for dead and unstable limbs, removing approximately 50 trees with a risk greater than 10, conducting structural inspections on all remaining trees with moderate to high risk, and hosting a community planting event in the weeks before the festival.

Conclusion

District 6 will benefit immensely from a Tree Safety & Fall Festival Initiative. Our initiative will not only promote the celebration of our district’s trees and the important work of NYC’s Parks and recreation department, but will also improve the overall safety of the highly trafficked streets. Our initiative will garner community support and appreciation for our trees and the employees of our city.